Machine Vision Enabled Bot for Object Tracking

Rupali Patil; Adhish Velingkar; Mohammad Nomaan Parmar; Shubham Khandhar; Bhavin Prajapati

doi:10.35877/454RI.jinav155

Authors

Rupali Patil KJ Somaiya college of Engineering, Mumbai, India
Adhish Velingkar KJ Somaiya College of Engineering, Mumbai-400077, India https://orcid.org/0000-0002-6952-7236
Mohammad Nomaan Parmar KJ Somaiya College of Engineering, Mumbai - 400077, India
Shubham Khandhar KJ Somaiya College of Engineering, Mumbai-400077, India
Bhavin Prajapati KJ Somaiya College of Engineering, Mumbai-400077, India

DOI:

https://doi.org/10.35877/454RI.jinav155

Keywords:

Raspberry-Pi, Pan-Tilt, Tracking, Tensor Flow, Open CV, Webpage

Abstract

Object detection and tracking are essential and testing undertaking in numerous PC vision appliances. To distinguish the object first find a way to accumulate information. In this design, the robot can distinguish the item and track it just as it can turn left and right position and afterward push ahead and in reverse contingent on the object motion. It keeps up the consistent separation between the item and the robot. We have designed a webpage that is used to display a live feed from the camera and the camera can be controlled by the user efficiently. Implementation of machine learning is done for detection purposes along with open cv and creating cloud storage. The pan-tilt mechanism is used for camera control which is attached to our 3-wheel chassis robot through servo motors. This idea can be used for surveillance purposes, monitoring local stuff, and human-machine interaction.

References

Lopes, M., & Santos-Victor, J. (2005). Visual learning by imitation with motor representations. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 35(3), 438-449.

Ju, Z., Yang, C., Li, Z., Cheng, L., & Ma, H. (2014, September). Teleoperation of humanoid baxter robot using haptic feedback. In 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI) (pp. 1-6). IEEE.

Konidaris, G., Kuindersma, S., Grupen, R., & Barto, A. (2012). Robot learning from demonstration by constructing skill trees. The International Journal of Robotics Research, 31(3), 360-375.

Kober, J., & Peters, J. (2010). Imitation and reinforcement learning. IEEE Robotics & Automation Magazine, 17(2), 55-62.